Logo: to the web site of Uppsala University

Load profiles derived from the growing amount of smart meter electricity consumption data are increasingly being sought. They can be valuable to public entities and grid operators for purposes such as operational planning, analyzing the co-location of users with smoothing profiles, dimensioning distribution grids, procuring distributed energy resources (DER) such as batteries or PV arrays, and implementing demand response projects. The challenge lies in analyzing the vast amount of available data in ways that make it useful - preferably to a broader audience. This article presents a straightforward and adaptable framework for creating typical load profiles (TLPs). The framework applies two normalizations across two clustering steps: k-Means, to identify prominent patterns in specific data subsets, and k-Modes, to recombine the identified subset patterns into a yearly-coherent and re-scalable TLP in an innovative way. The framework is demonstrated using real data of roughly 60,000 daily time series from around 40 public elementary schools in Uppsala Municipality, Sweden. The results showed that the framework could efficiently differentiate the load behaviors of the studied users over the year. Their largest load differences occurred midday and during weekends, although most studied users exhibited a similar behavior. The load magnitude was also shown to have a usable linear relationship with the schools' heated indoor area, enabling the use of TLPs to estimate the load of a new arbitrary school. Due to its design and versatility, the presented framework can serve as a valuable tool for identifying prominent patterns and supporting relevant decision-making processes.

As an increasing number of public actors both in the EU and in Sweden-from the highest political level to local operational levels-request greater flexibility and understanding of electrical power systems, additional methods and tools are necessary to identify potentially valuable sources of flexibility. In this study, we developed the Flexibility Value Index (FVI) to quantify and rank the system perspective-value of different electricity users' potential flexibility. Previous studies have mainly concentrated on either broad system levels, assessing total flexibility across an entire sector, country, or technology, or on a very detailed level, analyzing individual buildings based on specific appliance data on a case-by-case basis. However, there is a lack of studies between these two sides that, with limited information, aim to identify users whose consumption behavior or patterns would be valuable to change. This paper presents a quantification method based on five indicators demonstrated by evaluating electricity users from seven categories in Uppsala Municipality, Sweden. The results showed that the largest average FVIs were typically found in the later afternoon and early evenings during weekdays, coinciding with higher grid loads, but also in the mornings and weekends for individual users. Grid owners, public entities, or private businesses can use the developed index in strategic decision-making to identify users who might provide the most valuable flexibility for the power system for further investigations.

The ongoing transition to a net-zero emissions energy system is driven by what can be called a fourth wave of electrification, where fossil fuel-dependent processes are increasingly replaced by electric-powered appliances. At the same time, a substantial rise in electricity production is coming from renewable energy sources. Both developments are critical for a successful energy transition over the coming decades. A key aspect of this transition is understanding the load demand of electricity users.

The aim of this thesis is to analyze and assess the impact of different electricity users on the local grid, focusing on their consumption behavior. Conducted in collaboration with Uppsala Municipality, the research emphasizes a public perspective, user-friendliness, and a technologically-agnostic presentation of results. Three research objectives have been pursued throughout the thesis: (1) analyzing the impact of a single user's behavior in detail on the grid, incorporating peak shaving and distributed energy resources, in addition to a broader analysis to assess multiple users through a peak load correlation analysis; (2) proposing a framework to generate typical load profiles for various users, using time series clustering and a qualitative clustering step to model their demand patterns throughout the year; and (3) designing an index to quantify and compare the flexibility potential of electricity users, based on a rankable index developed using limited information.        

The results reveal considerable variation among users in both peak load contributions and flexibility potential. The peak load analysis and the flexibility index both identify users that could benefit from behavioral changes or offer potentially valuable flexibility to support grid stability. To test the framework of generating typical load profiles, a case study of elementary schools in Uppsala Municipality was conducted that showed that these institutions exhibited similar load patterns. The most typical load profile was rescaled to represent load variability based on the heated indoor area of an arbitrary school, showing a reasonable accuracy. These profiles can inform the selection of distributed energy resources, such as photovoltaic systems, or facilitate the co-location of users with complementary consumption patterns to minimize grid impacts.

The methods, tools, and frameworks presented in this thesis are versatile and can serve as valuable inputs for strategic decision-making by municipalities, businesses, and other stakeholders. By providing insights into user behavior, these tools can guide focused interventions to support the continued evolution of the energy transition.

Flexibility has increasingly gained attention within the field of electrification and energy transition where a common objective is to reduce the electricity consumption peaks. However, flexibility can increase the risk of grid congestion depending on where and when and it is used, thus an overall system perspective needs to be considered to ensure an effective energy transition. This paper presents a framework to assess electricity users' contributions to grid load peaks by splitting electricity consumption data into subsets based on time and temperature. The data in each subset is separately correlated with the grid load using three correlation measures to assess how the user's consumption changes at the same time as typical grid peak loads occur. The framework is implemented on four different types of business activities at Uppsala municipality in Sweden, which is a large public entity, to explore their behaviors and assess their grid peak load contributions. The results of this study conclude that all four activities generally contribute to the grid peak loads, but that differences exist. These differences are not visible without splitting the data, and not doing so can lead to unrepresentative conclusions. The presented framework can identify activities that contribute the most to unfavorable grid peaks, providing a tool for decision-makers to enable an accelerated energy transition.

As the global transition away from fossil fuels accelerates, energy systems across the globe face a significant challenge. Given the high energy consumption of electric vehicle chargers, effective control is imperative to prevent local grid overload and congestion. In Uppsala, Sweden, a newly built parking garage includes 30 electric vehicle chargers, 62 kW solar energy production, and a 60 kW/137 kWh battery energy storage system. This paper presents a control algorithm that uses a negative correlation scheme, adjusted to the local grid load, to effectively manage the battery energy storage. To improve the performance of the algorithm, a genetic optimization method is applied to find the best feasible daily load profile for the parking garage. The results indicate that peak load and energy consumption during grid high-load hours can be significantly reduced. This also results in an 9.5−12.8% reduction in electricity distribution fees at current prices as well as a peak load reduction of up to 50 %. Increasing the battery capacity and charging/discharging power in the scenarios analysed within the study will improve the algorithm’s ability to achieve a satisfactory negative correlation between the load demand of the facility and the local grid. The proposed control algorithm lowers the facility’s impact on the local grid during high-load peak hours by utilizing the battery energy storage system at the parking garage. Moreover, it decreases the distribution fees of the facility by lowering the load peaks and shifting the electricity consumption to the morning and night.

The power grid faces a rising challenge of increasing variability due to the integration of intermittent renewable energy sources (RES) and the connection of more and new types of loads. This development heightens the risk of both capacity shortage and grid congestion, addressing the need to complement traditional grid extension, which is expensive and can take a long time. A promising approach is load flexibility, which is the ability of an electricity user to adjust its consumption during a set time interval. This study proposes a Flexibility Value Index (FVI) to rank electricity users based on the value of their potential flexibility. The FVI utilizes three indicators derived from a user's consumption and the local grid's load. The FVI is demonstrated on seven test profiles, followed by ranking five different types of users from Uppsala Municipality, Sweden, during winter working days. The study reveals a spread in the FVI, and the ranked list enables a public entity or a grid owner to focus resources on the users that can potentially realize the most flexibility. Furthermore, the FVI can be utilized on the production from RES, indicating which might be a suitable match to enhance the grid's hosting capacity.

Energy storage systems (ESS) are suitable for many grid applications, where some have large seasonal variations. By combining additional services, i.e., service stacking, companies with energy storage assets may generate additional revenue as well as provide services to the power system. This paper aims to highlight and estimate the technical and economic potential of stacking services using energy storage systems in congested a distribution grid. A scheduling optimization tool was implemented for a large-scale battery ESS providing a selection of deemed relevant services over two years. The results show that there is a great potential to enhance the value of the ESS significantly depending on the service portfolio layout. A capacity life loss model was also implemented to analyse the degradation of the ESS, and the cycle aging was estimated for the chosen portfolios. The results showed that a fully stacked portfolio does not necessarily result in increased cycle aging, but that it depended on the services in the portfolio. The cycle aging was affected the most by stacking flexibility with energy arbitrage, while frequency regulation services only resulted in a few additional cycles during each year of operation. 

The clean energy transition is expected to continue at a fast pace in the upcoming years and will imply connection of large amounts of distributed energy resources to existing distribution grids. These have the opportunity to support the power system both locally but also regionally and possibly on a system level too, which could be achieved by bundling services i.e., service stacking. The aim of this article is to compare effects on distribution grids when implementing service stacked portfolios for centralized and distributed storage capacities in congested distribution grids. The complex nature of the scheduling optimization problem motivated using a non-linear solver and for this study a meta-heuristic approach was chosen. A large number of energy storage units were connected to the IEEE European Low Voltage test feeder and load flow calculations were executed using the open-source distribution grid simulator OpenDSS. The results indicate that service stacking could be implemented successfully for both centralized and distributed storage capacities, but the possibility to target local and regional power quality measures varies for the two cases. Finally, multi-service provision using energy storage systems should be considered in more extensive simulation and real-world studies to fully capture the effects on grid dynamics and scheduling possibilities. 

Energy storage systems are widely used for power system applications. By implementing service stacking, enhanced performance of storage systems can potentially be obtained. A scheduling tool based on linear programming was implemented to schedule a grid connected energy storage for two portfolios in separate periods. The results show that it is possible to provide additional services which generate value to the power system. By implementing a capacity loss life model the increased cycle aging is estimated. 

Congestion management in distribution grids is a growing challenge for network operators due to a combination of fast growing cities and electrification of transport and industry sectors. Energy storage systems connected downstream of bottlenecks have the potential to relieve parts of the peak demand, and typically does not motivate investments financially as a third-party actor alone. By stacking services it is possible for storage units to become financially viable while also enabling opportunities for providing additional local services and participation in markets for ancillary services. In this study, a large customer connected to the congested distribution grid in Uppsala, Sweden, was considered and the systematic potential of a battery connected to the facility was evaluated. A set of portfolios including the services tariff optimization, flexibility-as-a-service, and frequency containment reserve was evaluated by finding the optimal scheduling strategy using a metaheuristic optimization approach together with a capacity loss life model. The analysis was done for two separate years to illustrate the impact of yearly market fluctuations on the service allocation. Additionally, a sensitivity analysis was also performed for the rated energy capacity of the battery. The simulation results indicate that stacking services yields the most valuable portfolio with respect to both technical and economic value where the battery managed to capture several revenue streams at the cost of a small increase in cycle aging, compared to when using the BESS for tariff optimization only. From a system perspective, stacking services enables the BESS to support the power system and the local distribution grid during a large majority of the year.